Aims and Fit of Module

In the field of robotics, deep neural networks play a crucial role in computer vision systems. By using convolutional neural networks and other advanced techniques, robots can recognize and classify objects, track movements, and navigate through complex environments. Therefore, students studying robotics must have a solid understanding of deep neural networks in computer vision systems to design and develop advanced robotic systems that can perceive the world around them with greater accuracy and reliability. Learning about deep neural networks will equip students with the knowledge and skills needed to train these networks, optimize their performance, and apply them to real-world robotics applications. Furthermore, as the use of robotics and automation continues to expand across various industries, the demand for skilled professionals with expertise in deep neural networks and computer vision systems will only continue to grow. 
 
This module aims to enable students to: 
- Understand computer vision principles and techniques in robotics industries. 
- Explore deep learning algorithms for computer vision in robotics. 
- Apply computer vision and deep learning to robotics.

Learning outcomes

A	Demonstrate a nuanced understanding of both the theoretical and practical aspects of computer vision, distinguishing between various methodologies and their applications.

B	Demonstrate knowledge of image formation and implementation for image matching, recognition, and object detection with conventional computer vision technologies.

C	Demonstrate understanding on applying theoretical knowledge of deep learning computer vision systems to solve real-world problems.

D	Develop capabilities to analyze and evaluate performance of deep learning models and algorithms by accessing strengths and limitations with possible modifications and improvements based on students understanding of underlying principles.

E	Develop practical deep learning solutions by hands-on experience to design and implement deep learning pipelines from data preprocessing, training, selecting, and building network, validation, and trained-model evaluation.

F	Train students’ skills and experience in remote operation of Linux GPU servers via terminals.

Method of teaching and learning

The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments. 
 
This module is delivered with a combination of delivery in lectures, laboratory exercise, tutorials and a seminar at the end of the delivery. 
 
The concepts introduced during the lecture are illustrated using step-by-step analysis of practical training, complete case studies and live programming tutorials. 
 
In the laboratory practice, students will have opportunities to solve a set of exercises during the laboratories under the supervision of the lecturer and the teaching assistant. 
 
At the end of each week, there will be a tutorial to emphasize keynotes that have been discussed in lectures and laboratory practice during that week.  
 
At the end of the delivery, there will be a seminar to review the whole module delivery.